Todo lo que no te dirán, Mongo: Desarmando los mythscapes canadienses con la literatura de Dany Laferriére
Bibliographic record
Abstract
En este trabajo, se busca contribuir a desarmar los principales mythscapes nacionales canadienses, el multiculturalismo y el interculturalismo, usando Tout ce qu’on ne te dira pas Mongo (Todo lo que no te dirán Mongo) de Dany Laferrière, un escritor haitiano establecido en Montreal. El autor parte de la hipótesis de que la novela ofrece elementos de información incompatibles con dichos mythscapes nacionales, por lo cual constituye una oportunidad de aprendizaje. Inspirándose en el giro de las movilidades, así como en las teorías de los regímenes de movilidad, el autor acude a los estudios literarios para cuestionar la imagen estereotipada propagada por actores sociales de poder representados por los aparatos estatales de Canadá y Quebec, con el afán de aprovechar la fuga de cerebro para fortalecer su ventaja competitiva en un mercado globalizado.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".